Literature DB >> 29846803

Quantitative characterisation of clinically significant intra-prostatic cancer by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11.

Liran Domachevsky1, Natalia Goldberg2, Hanna Bernstine2,3, Meital Nidam2, David Groshar2,3.   

Abstract

OBJECTIVES: To quantitatively characterize clinically significant intra-prostatic cancer (IPC) by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11 positron emission tomography/magnetic resonance (PET/MR).
METHODS: Retrospective study approved by the institutional review board with informed written consent obtained. Patients with a solitary, biopsy-proven prostate cancer, Gleason score (GS) ≥7, presenting for initial evaluation by PET/computerised tomography (PET/CT), underwent early prostate PET/MR immediately after PSMA-11 tracer injection. PET/MR [MRI-based attenuation correction (MRAC)] and PET/CT [CT-based AC (CTAC)] maximal standardised uptake value (SUVmax) and minimal and mean apparent diffusion coefficient (ADCmin, ADCmean; respectively) in normal prostatic tissue (NPT) were compared to IPC area. The relationship between SUVmax, ADCmin and ADCmean measurements was obtained.
RESULTS: Twenty-two patients (mean age 69.5±5.0 years) were included in the analysis. Forty-four prostate areas were evaluated (22 IPC and 22 NPT). Median MRAC SUVmax of NPT was significantly lower than median MRAC SUVmax of IPC (p < 0.0001). Median ADCmin and ADCmean of NPT was significantly higher than median ADCmin and ADCmean of IPC (p < 0.0001). A very good correlation was found between MRAC SUVmax with CTAC SUVmax (rho = -0.843, p < 0.0001). A good inverse relationship was found between MRAC SUVmax and CTAC SUVmax with ADCmin (rho = -0.717, p < 0.0001 and -0.740, p < 0.0001; respectively; Z = 0.22, p = 0.82, NS) and with MRAC SUVmax and ADCmean (rho = -0.737, p < 0.0001).
CONCLUSIONS: PET/MR SUVmax, ADCmin and ADCmean are distinct biomarkers able to differentiate between IPC and NPT in naïve prostate cancer patients with GS ≥ 7. KEY POINTS: • PSMA PET/MR metrics differentiate between normal and tumoural prostatic tissue. • A multi-parametric approach combining molecular and anatomical information might direct prostate biopsy. • PSMA PET/MR metrics are warranted for radiomics analysis.

Entities:  

Keywords:  Apparent diffusion coefficient; Biomarkers; Magnetic resonance imaging; Positron emission tomography; Prostate cancer

Mesh:

Substances:

Year:  2018        PMID: 29846803     DOI: 10.1007/s00330-018-5484-1

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  31 in total

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Authors:  Stefan A Koerber; Maximilian T Utzinger; Clemens Kratochwil; Claudia Kesch; Matthias F Haefner; Sonja Katayama; Walter Mier; Andrei H Iagaru; Klaus Herfarth; Uwe Haberkorn; Juergen Debus; Frederik L Giesel
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2.  Validation of Prostate Imaging Reporting and Data System Version 2 Using an MRI-Ultrasound Fusion Biopsy in Prostate Cancer Diagnosis.

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4.  Prostate cancer radiomics and the promise of radiogenomics.

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5.  Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

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6.  Multiparametric MRI Apparent Diffusion Coefficient (ADC) Accuracy in Diagnosing Clinically Significant Prostate Cancer.

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7.  Assessment of PI-RADS v2 for the Detection of Prostate Cancer.

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8.  Comparison of bone scintigraphy and 68Ga-PSMA PET for skeletal staging in prostate cancer.

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9.  Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification.

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10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
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  8 in total

1.  Comparison between pelvic PSMA-PET/MR and whole-body PSMA-PET/CT for the initial evaluation of prostate cancer: a proof of concept study.

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Journal:  Eur Radiol       Date:  2019-07-22       Impact factor: 5.315

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Authors:  Rubel Chakravarty; Cerise M Siamof; Ashutosh Dash; Weibo Cai
Journal:  Am J Nucl Med Mol Imaging       Date:  2018-08-20

Review 3.  Multimodal imaging for radiation therapy planning in patients with primary prostate cancer.

Authors:  Constantinos Zamboglou; Matthias Eiber; Thomas R Fassbender; Matthias Eder; Simon Kirste; Michael Bock; Oliver Schilling; Kathrin Reichel; Uulke A van der Heide; Anca L Grosu
Journal:  Phys Imaging Radiat Oncol       Date:  2018-11-05

Review 4.  Radiomics in prostate cancer: an up-to-date review.

Authors:  Matteo Ferro; Ottavio de Cobelli; Gennaro Musi; Francesco Del Giudice; Giuseppe Carrieri; Gian Maria Busetto; Ugo Giovanni Falagario; Alessandro Sciarra; Martina Maggi; Felice Crocetto; Biagio Barone; Vincenzo Francesco Caputo; Michele Marchioni; Giuseppe Lucarelli; Ciro Imbimbo; Francesco Alessandro Mistretta; Stefano Luzzago; Mihai Dorin Vartolomei; Luigi Cormio; Riccardo Autorino; Octavian Sabin Tătaru
Journal:  Ther Adv Urol       Date:  2022-07-04

5.  A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

Authors:  Jeroen Bleker; Thomas C Kwee; Dennis Rouw; Christian Roest; Jaap Borstlap; Igle Jan de Jong; Rudi A J O Dierckx; Henkjan Huisman; Derya Yakar
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6.  Prostate cancer evaluation using PET quantification in 68Ga-PSMA-11 PET/MR with attenuation correction of bones as a fifth compartment.

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Journal:  Quant Imaging Med Surg       Date:  2020-01

7.  An international expert opinion statement on the utility of PET/MR for imaging of skeletal metastases.

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-02-22       Impact factor: 9.236

8.  Establishment and prospective validation of an SUVmax cutoff value to discriminate clinically significant prostate cancer from benign prostate diseases in patients with suspected prostate cancer by 68Ga-PSMA PET/CT: a real-world study.

Authors:  Jianhua Jiao; Fei Kang; Jingliang Zhang; Zhiyong Quan; Weihong Wen; Xiaohu Zhao; Shuaijun Ma; Peng Wu; Fa Yang; Wei Guo; Xiaojian Yang; Jianlin Yuan; Yongquan Shi; Jing Wang; Weijun Qin
Journal:  Theranostics       Date:  2021-07-25       Impact factor: 11.556

  8 in total

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